{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8aa8de0a",
   "metadata": {
    "papermill": {
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     "exception": false,
     "start_time": "2025-07-17T23:51:12.593294",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 巴西电商用户分析：留存与简版LTV预测 / Brazilian E‑commerce User Analysis: Cohort Retention & Simplified LTV Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "849003bc",
   "metadata": {
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     "end_time": "2025-07-17T23:51:12.616263",
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     "start_time": "2025-07-17T23:51:12.609271",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 0. 环境与数据 / Environment and Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced5b9c2",
   "metadata": {
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     "start_time": "2025-07-17T23:51:12.624052",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 0.1 说明 / Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9319fd34",
   "metadata": {
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     "start_time": "2025-07-17T23:51:12.639583",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "本 Notebook 在 Kaggle 平台运行。\n",
    "- Notebook 链接：[https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-user-analysis-cohort-retenti](https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-user-analysis-cohort-retenti)\n",
    "\n",
    "---\n",
    "\n",
    "This notebook runs on the Kaggle platform.\n",
    "- Notebook Link: [https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-user-analysis-cohort-retenti](https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-user-analysis-cohort-retenti)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad90228b",
   "metadata": {
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     "end_time": "2025-07-17T23:51:12.661922",
     "exception": false,
     "start_time": "2025-07-17T23:51:12.654685",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "前作（用户分群）：[https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-customer-segmentation](https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-customer-segmentation)\n",
    "\n",
    "---\n",
    "\n",
    "Previous Work (Customer Segmentation): [https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-customer-segmentation](https://www.kaggle.com/code/yiquanxiao/brazilian-e-commerce-customer-segmentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b72e337a",
   "metadata": {
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     "start_time": "2025-07-17T23:51:12.669238",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 0.2 依赖 / Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce637647",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2025-07-17T23:51:22.119219Z",
     "shell.execute_reply": "2025-07-17T23:51:22.118216Z"
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "from sklearn.model_selection import train_test_split\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import joblib\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0945d1",
   "metadata": {
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     "duration": 0.006971,
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     "exception": false,
     "start_time": "2025-07-17T23:51:22.128453",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1. 数据与模型加载 / Data & Model Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92782482",
   "metadata": {
    "papermill": {
     "duration": 0.006965,
     "end_time": "2025-07-17T23:51:22.149643",
     "exception": false,
     "start_time": "2025-07-17T23:51:22.142678",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1.1 加载原始 CSV 数据 / Load CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a77105a0",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_path = Path('/kaggle/input/brazilian-ecommerce')\n",
    "# 客户基本信息 / Customer info\n",
    "customers = pd.read_csv(data_path / 'olist_customers_dataset.csv')\n",
    "# 订单明细（含购买时间）/ Orders (with purchase timestamp)\n",
    "orders = pd.read_csv(data_path / 'olist_orders_dataset.csv')\n",
    "# 支付记录（含支付金额）/ Payments (with payment value)\n",
    "payments = pd.read_csv(data_path / 'olist_order_payments_dataset.csv')\n",
    "# 额外加载 order_items & products，用于品类特征 / Load for product category\n",
    "order_items = pd.read_csv(data_path / 'olist_order_items_dataset.csv')\n",
    "products = pd.read_csv(data_path / 'olist_products_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e60ea1",
   "metadata": {
    "papermill": {
     "duration": 0.008421,
     "end_time": "2025-07-17T23:51:23.679629",
     "exception": false,
     "start_time": "2025-07-17T23:51:23.671208",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 1.2 加载预训练对象（scaler.pkl, kmeans_model.pkl）/ Load scaler & K‑Means model\n",
    "\n",
    "Here, we use the pretrained scaler and KMeans model from previous work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57ea8b0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:23.696740Z",
     "iopub.status.busy": "2025-07-17T23:51:23.696429Z",
     "iopub.status.idle": "2025-07-17T23:51:24.120882Z",
     "shell.execute_reply": "2025-07-17T23:51:24.120007Z"
    },
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     "end_time": "2025-07-17T23:51:24.122455",
     "exception": false,
     "start_time": "2025-07-17T23:51:23.687488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models_path  = Path('/kaggle/input/brazilian-e-commerce-customer-segmentation')\n",
    "scaler = joblib.load(models_path / 'scaler.pkl')\n",
    "kmeans_model = joblib.load(models_path / 'kmeans_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504b3ee5",
   "metadata": {
    "papermill": {
     "duration": 0.007021,
     "end_time": "2025-07-17T23:51:24.137381",
     "exception": false,
     "start_time": "2025-07-17T23:51:24.130360",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. 数据预处理 / Data Preprocessing  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6cc6da",
   "metadata": {
    "papermill": {
     "duration": 0.006917,
     "end_time": "2025-07-17T23:51:24.151560",
     "exception": false,
     "start_time": "2025-07-17T23:51:24.144643",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.1 数据清洗 / Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de7924a3",
   "metadata": {
    "execution": {
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     "start_time": "2025-07-17T23:51:24.159006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking customers ...\n",
      "# of duplicates: 0\n",
      "# of missing items:\n",
      "customer_id                 0\n",
      "customer_unique_id          0\n",
      "customer_zip_code_prefix    0\n",
      "customer_city               0\n",
      "customer_state              0\n",
      "dtype: int64\n",
      "--------------------------------------------\n",
      "Checking orders ...\n",
      "# of duplicates: 0\n",
      "# of missing items:\n",
      "order_id                            0\n",
      "customer_id                         0\n",
      "order_status                        0\n",
      "order_purchase_timestamp            0\n",
      "order_approved_at                 160\n",
      "order_delivered_carrier_date     1783\n",
      "order_delivered_customer_date    2965\n",
      "order_estimated_delivery_date       0\n",
      "dtype: int64\n",
      "--------------------------------------------\n",
      "Checking payments ...\n",
      "# of duplicates: 0\n",
      "# of missing items:\n",
      "order_id                0\n",
      "payment_sequential      0\n",
      "payment_type            0\n",
      "payment_installments    0\n",
      "payment_value           0\n",
      "dtype: int64\n",
      "--------------------------------------------\n",
      "Checking order_items ...\n",
      "# of duplicates: 0\n",
      "# of missing items:\n",
      "order_id               0\n",
      "order_item_id          0\n",
      "product_id             0\n",
      "seller_id              0\n",
      "shipping_limit_date    0\n",
      "price                  0\n",
      "freight_value          0\n",
      "dtype: int64\n",
      "--------------------------------------------\n",
      "Checking products ...\n",
      "# of duplicates: 0\n",
      "# of missing items:\n",
      "product_id                      0\n",
      "product_category_name         610\n",
      "product_name_lenght           610\n",
      "product_description_lenght    610\n",
      "product_photos_qty            610\n",
      "product_weight_g                2\n",
      "product_length_cm               2\n",
      "product_height_cm               2\n",
      "product_width_cm                2\n",
      "dtype: int64\n",
      "--------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 检查缺失值及重复 / Check missing values and duplicates\n",
    "for df_name, df in [(\"customers\", customers), \n",
    "                    (\"orders\", orders), \n",
    "                    (\"payments\", payments), \n",
    "                    (\"order_items\", order_items), \n",
    "                    (\"products\", products)]:\n",
    "    print(f\"Checking {df_name} ...\")\n",
    "    print(f\"# of duplicates: {df.duplicated().sum()}\")\n",
    "    # print(f\"# of duplicates: {df[key].duplicated().sum()}\")\n",
    "    print(f\"# of missing items:\")\n",
    "    print(df.isnull().sum())\n",
    "    print(\"--------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b303156b",
   "metadata": {
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     "exception": false,
     "start_time": "2025-07-17T23:51:24.681997",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "没有重复值，出现了一些缺失值。但是对于我们关注的那几列来说，只有`product_category_name`有重复的情况。所以只需要处理这个就好了。\n",
    "\n",
    "There are no duplicate values, but some missing values have appeared. However, for the columns we're focusing on, only `product_category_name` has duplicates. So we only need to handle that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ba38251",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 处理缺失值 / Handling missing value\n",
    "products['product_category_name'] = products['product_category_name'].fillna(\"unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4276a3d",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 3 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   customer_id         99441 non-null  object\n",
      " 1   customer_unique_id  99441 non-null  object\n",
      " 2   customer_state      99441 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 2.3+ MB\n",
      "None\n",
      "-----------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 3 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   order_id                  99441 non-null  object\n",
      " 1   customer_id               99441 non-null  object\n",
      " 2   order_purchase_timestamp  99441 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 2.3+ MB\n",
      "None\n",
      "-----------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 103886 entries, 0 to 103885\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   order_id       103886 non-null  object \n",
      " 1   payment_value  103886 non-null  float64\n",
      " 2   payment_type   103886 non-null  object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 2.4+ MB\n",
      "None\n",
      "-----------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count   Dtype \n",
      "---  ------      --------------   ----- \n",
      " 0   order_id    112650 non-null  object\n",
      " 1   product_id  112650 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.7+ MB\n",
      "None\n",
      "-----------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 2 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   product_id             32951 non-null  object\n",
      " 1   product_category_name  32951 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 515.0+ KB\n",
      "None\n",
      "-----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 只保留必要字段 / Select only required columns\n",
    "customers = customers[['customer_id', 'customer_unique_id', 'customer_state']]\n",
    "orders = orders[['order_id', 'customer_id', 'order_purchase_timestamp']]\n",
    "payments = payments[['order_id', 'payment_value', 'payment_type']]\n",
    "order_items = order_items[['order_id', 'product_id']]\n",
    "products = products[['product_id', 'product_category_name']]\n",
    "\n",
    "for df in [customers, orders, payments, order_items, products]: \n",
    "    print(df.info())\n",
    "    print(\"-----------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56d12ec5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:24.858765Z",
     "iopub.status.busy": "2025-07-17T23:51:24.858504Z",
     "iopub.status.idle": "2025-07-17T23:51:24.893891Z",
     "shell.execute_reply": "2025-07-17T23:51:24.892937Z"
    },
    "papermill": {
     "duration": 0.045618,
     "end_time": "2025-07-17T23:51:24.895565",
     "exception": false,
     "start_time": "2025-07-17T23:51:24.849947",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 转换订单的时间列为 datetime / Convert order timestamp columns to datetime\n",
    "orders['order_purchase_timestamp'] = pd.to_datetime(orders['order_purchase_timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c38e6d",
   "metadata": {
    "papermill": {
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     "end_time": "2025-07-17T23:51:24.912129",
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     "start_time": "2025-07-17T23:51:24.903396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.2 合并核心表格 / Merge core tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86ce50db",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2025-07-17T23:51:24.929092Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 合并 orders 与 payments / Merge orders & payments\n",
    "orders_payments = orders.merge(payments, on='order_id', how='left')\n",
    "\n",
    "# 合并 customers 获取 unique_id 和地区 / Merge customers for unique_id & state\n",
    "orders_payments = orders_payments.merge(\n",
    "    customers, on='customer_id', how='left'\n",
    ")\n",
    "\n",
    "# 合并 order_items 与 products，后续可聚合品类特征 / Merge order_items & products for category\n",
    "order_items_products = order_items.merge(\n",
    "    products, on='product_id', how='left'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d1f24e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:25.166949Z",
     "iopub.status.busy": "2025-07-17T23:51:25.166560Z",
     "iopub.status.idle": "2025-07-17T23:51:25.218738Z",
     "shell.execute_reply": "2025-07-17T23:51:25.217589Z"
    },
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     "duration": 0.062846,
     "end_time": "2025-07-17T23:51:25.220522",
     "exception": false,
     "start_time": "2025-07-17T23:51:25.157676",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_id                    0\n",
      "customer_id                 0\n",
      "order_purchase_timestamp    0\n",
      "payment_value               1\n",
      "payment_type                1\n",
      "customer_unique_id          0\n",
      "customer_state              0\n",
      "dtype: int64\n",
      "----------------------------------\n",
      "order_id                 0\n",
      "product_id               0\n",
      "product_category_name    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 检查 merge 后可能出现的缺失值 / Check possible missing values after merge\n",
    "print(orders_payments.isnull().sum())\n",
    "print(\"----------------------------------\")\n",
    "print(order_items_products.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0289c780",
   "metadata": {
    "papermill": {
     "duration": 0.007415,
     "end_time": "2025-07-17T23:51:25.235762",
     "exception": false,
     "start_time": "2025-07-17T23:51:25.228347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "可以看到 merge 之后存在缺失值的情况。这里，我们打算用下面的策略来填补：\n",
    "\n",
    "- 对于 `payment_type`：将缺失值填补为 `\"unknown\"`，表示该订单缺少支付方式信息；\n",
    "- 对于 `payment_value`：我们使用对应 `customer_state` 的中位数进行填补，假设不同地区用户的支付金额存在结构性差异。\n",
    "\n",
    "---\n",
    "\n",
    "We observe that there are missing values after merging the tables. To address this, we apply the following imputation strategies:\n",
    "\n",
    "- For `payment_type`: Missing values are filled with `\"unknown\"` to indicate that no payment method was recorded for the order;\n",
    "- For `payment_value`: Missing values are imputed using the median payment value for the corresponding `customer_state`, assuming regional differences in customer payment behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6df93c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:25.252285Z",
     "iopub.status.busy": "2025-07-17T23:51:25.252028Z",
     "iopub.status.idle": "2025-07-17T23:51:25.267852Z",
     "shell.execute_reply": "2025-07-17T23:51:25.266912Z"
    },
    "papermill": {
     "duration": 0.026067,
     "end_time": "2025-07-17T23:51:25.269474",
     "exception": false,
     "start_time": "2025-07-17T23:51:25.243407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 计算每个 customer_state 对应的 payment_value 中位数\n",
    "# Compute median payment_value per customer_state\n",
    "state_medians = (\n",
    "    orders_payments\n",
    "    .groupby('customer_state')['payment_value']\n",
    "    .median()\n",
    ")\n",
    "\n",
    "# 定义填补函数\n",
    "# Define imputation function based only on customer_state\n",
    "def fill_by_state(row):\n",
    "    if pd.notnull(row['payment_value']):\n",
    "        return row['payment_value']\n",
    "    \n",
    "    state = row['customer_state']\n",
    "    \n",
    "    # 如果 state 存在中位数，填入；否则使用全局中位数或 0\n",
    "    if state in state_medians.index:\n",
    "        return state_medians.loc[state]\n",
    "    return orders_payments['payment_value'].median()  # or return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a73d3e2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:25.286754Z",
     "iopub.status.busy": "2025-07-17T23:51:25.286465Z",
     "iopub.status.idle": "2025-07-17T23:51:26.351545Z",
     "shell.execute_reply": "2025-07-17T23:51:26.350528Z"
    },
    "papermill": {
     "duration": 1.07556,
     "end_time": "2025-07-17T23:51:26.353015",
     "exception": false,
     "start_time": "2025-07-17T23:51:25.277455",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order_id                    0\n",
      "customer_id                 0\n",
      "order_purchase_timestamp    0\n",
      "payment_value               0\n",
      "payment_type                0\n",
      "customer_unique_id          0\n",
      "customer_state              0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 处理缺失值 / Handling missing value\n",
    "orders_payments['payment_type'] = orders_payments['payment_type'].fillna(\"unknown\")\n",
    "orders_payments['payment_value'] = orders_payments.apply(fill_by_state, axis=1)\n",
    "print(orders_payments.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5b69fb",
   "metadata": {
    "papermill": {
     "duration": 0.007654,
     "end_time": "2025-07-17T23:51:26.369926",
     "exception": false,
     "start_time": "2025-07-17T23:51:26.362272",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.3 生成 RFM 特征 / Prepare RFM features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "575a1e3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:26.387004Z",
     "iopub.status.busy": "2025-07-17T23:51:26.386591Z",
     "iopub.status.idle": "2025-07-17T23:51:33.450642Z",
     "shell.execute_reply": "2025-07-17T23:51:33.449874Z"
    },
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     "end_time": "2025-07-17T23:51:33.452207",
     "exception": false,
     "start_time": "2025-07-17T23:51:26.377775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "                 customer_unique_id    R  F       M\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  160  1  141.90\n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  163  1   27.19\n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算基准日期 / Reference date = last purchase timestamp\n",
    "reference_date = orders_payments['order_purchase_timestamp'].max()\n",
    "\n",
    "# 按用户计算 R、F、M / Compute Recency, Frequency, Monetary\n",
    "rfm = (\n",
    "    orders_payments\n",
    "    .groupby('customer_unique_id')\n",
    "    .agg(\n",
    "        R = ('order_purchase_timestamp', lambda x: (reference_date - x.max()).days),\n",
    "        F = ('order_id', 'nunique'),\n",
    "        M = ('payment_value', 'sum')\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e32027f",
   "metadata": {
    "papermill": {
     "duration": 0.007948,
     "end_time": "2025-07-17T23:51:33.468461",
     "exception": false,
     "start_time": "2025-07-17T23:51:33.460513",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.4 应用 Scaler + 生成 K‑Means 簇标签 / Apply scaler & assign clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b3b713d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:33.485646Z",
     "iopub.status.busy": "2025-07-17T23:51:33.485373Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>R_z</th>\n",
       "      <th>F_z</th>\n",
       "      <th>M_z</th>\n",
       "      <th>cluster</th>\n",
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      "text/plain": [
       "                 customer_unique_id    R  F       M       R_z       F_z  \\\n",
       "0  0000366f3b9a7992bf8c76cfdf3221e2  160  1  141.90 -0.832621 -0.162368   \n",
       "1  0000b849f77a49e4a4ce2b2a4ca5be3f  163  1   27.19 -0.813067 -0.162368   \n",
       "2  0000f46a3911fa3c0805444483337064  585  1   86.22  1.937662 -0.162368   \n",
       "3  0000f6ccb0745a6a4b88665a16c9f078  369  1   43.62  0.529706 -0.162368   \n",
       "4  0004aac84e0df4da2b147fca70cf8255  336  1  196.89  0.314602 -0.162368   \n",
       "\n",
       "        M_z  cluster  \n",
       "0 -0.106697        1  \n",
       "1 -0.602360        1  \n",
       "2 -0.347291        0  \n",
       "3 -0.531366        0  \n",
       "4  0.130916        0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标准化 RFM 特征 / Scale RFM features\n",
    "rfm_raw = rfm.copy()\n",
    "rfm[['R_z', 'F_z', 'M_z']] = scaler.transform(rfm[['R', 'F', 'M']])\n",
    "\n",
    "# 预测簇标签并合并回 rfm / Predict cluster labels and merge back\n",
    "rfm['cluster'] = kmeans_model.predict(rfm[['R_z', 'F_z', 'M_z']])\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2f056e",
   "metadata": {
    "papermill": {
     "duration": 0.007893,
     "end_time": "2025-07-17T23:51:33.581124",
     "exception": false,
     "start_time": "2025-07-17T23:51:33.573231",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Cohort 留存分析 / Cohort Retention Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e269da5a",
   "metadata": {
    "papermill": {
     "duration": 0.00815,
     "end_time": "2025-07-17T23:51:33.598149",
     "exception": false,
     "start_time": "2025-07-17T23:51:33.589999",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.1 定义 Cohort（首购月份） / Define cohorts by first purchase month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e65b202c",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:33.616232Z",
     "iopub.status.busy": "2025-07-17T23:51:33.615966Z",
     "iopub.status.idle": "2025-07-17T23:51:33.821173Z",
     "shell.execute_reply": "2025-07-17T23:51:33.820052Z"
    },
    "papermill": {
     "duration": 0.215852,
     "end_time": "2025-07-17T23:51:33.822640",
     "exception": false,
     "start_time": "2025-07-17T23:51:33.606788",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重复购买率: 3.12%\n"
     ]
    }
   ],
   "source": [
    "order_counts = orders_payments.groupby('customer_unique_id')['order_id'].nunique()\n",
    "# 筛选出重复购买的用户（订单数 > 1）\n",
    "repeat_buyers = order_counts[order_counts > 1]\n",
    "# 统计重复购买用户数量\n",
    "num_repeat_buyers = repeat_buyers.shape[0]\n",
    "# 重复购买率\n",
    "total_users = order_counts.shape[0]\n",
    "repeat_rate = num_repeat_buyers / total_users\n",
    "print(f'重复购买率: {repeat_rate:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76443072",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:33.841600Z",
     "iopub.status.busy": "2025-07-17T23:51:33.841327Z",
     "iopub.status.idle": "2025-07-17T23:51:34.133124Z",
     "shell.execute_reply": "2025-07-17T23:51:34.132300Z"
    },
    "papermill": {
     "duration": 0.303191,
     "end_time": "2025-07-17T23:51:34.134789",
     "exception": false,
     "start_time": "2025-07-17T23:51:33.831598",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 创建订单月份字段 / extract order month\n",
    "orders_payments['order_month'] = orders_payments['order_purchase_timestamp'].dt.to_period('M').dt.to_timestamp()\n",
    "# 用户首购月份 / cohort month per user\n",
    "cohort_df = (\n",
    "    orders_payments\n",
    "    .groupby('customer_unique_id')['order_month']\n",
    "    .min()\n",
    "    .reset_index()\n",
    "    .rename(columns={'order_month': 'cohort_month'})\n",
    ")\n",
    "# 合并 cohort_month / merge back\n",
    "orders_payments = orders_payments.merge(cohort_df, on='customer_unique_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb47bc33",
   "metadata": {
    "papermill": {
     "duration": 0.007692,
     "end_time": "2025-07-17T23:51:34.150790",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.143098",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 计算每期留存率 / Calculate retention rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e077754",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:34.168049Z",
     "iopub.status.busy": "2025-07-17T23:51:34.167734Z",
     "iopub.status.idle": "2025-07-17T23:51:34.445361Z",
     "shell.execute_reply": "2025-07-17T23:51:34.444626Z"
    },
    "papermill": {
     "duration": 0.288179,
     "end_time": "2025-07-17T23:51:34.446950",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.158771",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 计算用户在每个订单的“期数” / period number since cohort\n",
    "orders_payments['period_number'] = (\n",
    "    (orders_payments['order_month'].dt.year - orders_payments['cohort_month'].dt.year) * 12 +\n",
    "    (orders_payments['order_month'].dt.month - orders_payments['cohort_month'].dt.month)\n",
    ")\n",
    "\n",
    "# 去重：每用户每期只算一次 / drop duplicate user-period\n",
    "user_period = orders_payments.drop_duplicates(\n",
    "    ['customer_unique_id', 'cohort_month', 'period_number']\n",
    ")\n",
    "\n",
    "# 引入簇标签 / attach cluster\n",
    "user_period = user_period.merge(\n",
    "    rfm[['customer_unique_id', 'cluster']],\n",
    "    on='customer_unique_id', how='left'\n",
    ")\n",
    "\n",
    "# 计算基期用户数 / number of users in period 0 for each cohort & cluster\n",
    "cohort_sizes = (\n",
    "    user_period[user_period['period_number'] == 0]\n",
    "    .groupby(['cohort_month', 'cluster'])['customer_unique_id']\n",
    "    .nunique()\n",
    "    .reset_index(name='n_users')\n",
    ")\n",
    "\n",
    "# 计算每期活跃用户数 / active users per period\n",
    "retention_counts = (\n",
    "    user_period\n",
    "    .groupby(['cohort_month', 'cluster', 'period_number'])['customer_unique_id']\n",
    "    .nunique()\n",
    "    .reset_index(name='active_users')\n",
    ")\n",
    "\n",
    "# 合并并计算留存率 / merge and compute retention rate\n",
    "retention = retention_counts.merge(\n",
    "    cohort_sizes, on=['cohort_month', 'cluster']\n",
    ")\n",
    "retention['retention_rate'] = retention['active_users'] / retention['n_users']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11beab74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:34.464985Z",
     "iopub.status.busy": "2025-07-17T23:51:34.464159Z",
     "iopub.status.idle": "2025-07-17T23:51:34.473323Z",
     "shell.execute_reply": "2025-07-17T23:51:34.472388Z"
    },
    "papermill": {
     "duration": 0.019712,
     "end_time": "2025-07-17T23:51:34.474849",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.455137",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cohort_month</th>\n",
       "      <th>cluster</th>\n",
       "      <th>period_number</th>\n",
       "      <th>active_users</th>\n",
       "      <th>n_users</th>\n",
       "      <th>retention_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>302</td>\n",
       "      <td>302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cohort_month  cluster  period_number  active_users  n_users  retention_rate\n",
       "0   2016-09-01        0              0             4        4        1.000000\n",
       "1   2016-10-01        0              0           302      302        1.000000\n",
       "2   2016-10-01        2              0            12       12        1.000000\n",
       "3   2016-10-01        2              6             1       12        0.083333\n",
       "4   2016-10-01        2              9             1       12        0.083333"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daead35f",
   "metadata": {
    "papermill": {
     "duration": 0.00866,
     "end_time": "2025-07-17T23:51:34.492132",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.483472",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.3 按 K‑Means 簇拆分留存对比 / Compare retention by cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c247592c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:34.510566Z",
     "iopub.status.busy": "2025-07-17T23:51:34.510288Z",
     "iopub.status.idle": "2025-07-17T23:51:34.515325Z",
     "shell.execute_reply": "2025-07-17T23:51:34.514653Z"
    },
    "papermill": {
     "duration": 0.015987,
     "end_time": "2025-07-17T23:51:34.516914",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.500927",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 过滤期数（ 0–12月 ） / limit to first year\n",
    "retention = retention[retention['period_number'] <= 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e147aafa",
   "metadata": {
    "papermill": {
     "duration": 0.008377,
     "end_time": "2025-07-17T23:51:34.533748",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.525371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.4 可视化留存热力图 / Visualize retention heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4d7b1f8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:34.551943Z",
     "iopub.status.busy": "2025-07-17T23:51:34.551579Z",
     "iopub.status.idle": "2025-07-17T23:51:36.111452Z",
     "shell.execute_reply": "2025-07-17T23:51:36.110379Z"
    },
    "papermill": {
     "duration": 1.570916,
     "end_time": "2025-07-17T23:51:36.113260",
     "exception": false,
     "start_time": "2025-07-17T23:51:34.542344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为每个 cluster 绘制热力图 / loop clusters\n",
    "for cl in sorted(retention['cluster'].unique()):\n",
    "    sub = retention[retention['cluster'] == cl].copy()\n",
    "    sub['cohort_month'] = sub['cohort_month'].dt.to_period('M').astype(str)\n",
    "    pivot = sub.pivot(index='cohort_month', columns='period_number', values='retention_rate')\n",
    "    # pivot = pivot.fillna(0)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.heatmap(pivot, annot=True, fmt=\".0%\", cmap='YlGnBu', mask=pivot.isnull())\n",
    "    plt.title(f'Cluster {cl} Cohort Retention')\n",
    "    plt.ylabel('Cohort Month')\n",
    "    plt.xlabel('Months Since Cohort')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "610c2be8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:36.143464Z",
     "iopub.status.busy": "2025-07-17T23:51:36.143169Z",
     "iopub.status.idle": "2025-07-17T23:51:36.166075Z",
     "shell.execute_reply": "2025-07-17T23:51:36.165052Z"
    },
    "papermill": {
     "duration": 0.039437,
     "end_time": "2025-07-17T23:51:36.167506",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.128069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0:\n",
      "   period_number  avg_retention_rate\n",
      "0              0                 1.0\n",
      "========================================\n",
      "Cluster 1:\n",
      "   period_number  avg_retention_rate\n",
      "0              0                 1.0\n",
      "========================================\n",
      "Cluster 2:\n",
      "    period_number  avg_retention_rate\n",
      "0               0            1.000000\n",
      "1               1            0.201917\n",
      "2               2            0.094717\n",
      "3               3            0.071690\n",
      "4               4            0.067504\n",
      "5               5            0.053421\n",
      "6               6            0.059035\n",
      "7               7            0.049019\n",
      "8               8            0.049072\n",
      "9               9            0.043716\n",
      "10             10            0.052900\n",
      "11             11            0.046611\n",
      "12             12            0.041562\n",
      "========================================\n",
      "Cluster 3:\n",
      "   period_number  avg_retention_rate\n",
      "0              0            1.000000\n",
      "1              1            0.013365\n",
      "2              2            0.010064\n",
      "3              3            0.005128\n",
      "4              5            0.014085\n",
      "5              6            0.013487\n",
      "========================================\n"
     ]
    }
   ],
   "source": [
    "# 为每个簇计算每期的平均留存率 / Calculate average retention rate per period for each cluster\n",
    "for cl in sorted(retention['cluster'].unique()):\n",
    "    sub = retention[retention['cluster'] == cl].copy()\n",
    "    sub['cohort_month'] = sub['cohort_month'].dt.to_period('M').astype(str)\n",
    "    avg_retention_rate = (\n",
    "        sub.groupby(['period_number'])['retention_rate'].mean().reset_index(name=\"avg_retention_rate\")\n",
    "    )\n",
    "    print(f\"Cluster {cl}:\")\n",
    "    print(avg_retention_rate)\n",
    "    print(\"========================================\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb64a942",
   "metadata": {
    "papermill": {
     "duration": 0.013696,
     "end_time": "2025-07-17T23:51:36.194867",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.181171",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 洞察 / Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2f0336",
   "metadata": {
    "papermill": {
     "duration": 0.013929,
     "end_time": "2025-07-17T23:51:36.222998",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.209069",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**簇 0 留存洞察：**\n",
    "\n",
    "- **留存表现 / Retention Behavior**  \n",
    "  - 期数 0（首月）留存 100%，之后所有期数复购率几乎为 0  \n",
    "  - 首购后即彻底流失，无二次行动迹象  \n",
    "\n",
    "- **画像回顾 / Profile Recap**  \n",
    "  - R ≈ 438 天：最后一次购买时间非常久远  \n",
    "  - F = 1 笔：仅发生过一次交易  \n",
    "  - M ≈ 135 元：低客单价  \n",
    "\n",
    "- **运营建议 / Actionable Recommendations**  \n",
    "  1. **深度“回流”优惠**：用户已流失一年以上，直接用“重回有礼”超大幅度折扣（如 $50\\%$ + 包邮）刺激一次性回购\n",
    "  2. **品牌唤醒广告**：在微信/社交媒体投放品牌回访广告，重新激发认知  \n",
    "  3. **流失原因调研**：发放“流失调查”问卷，小规模采样获取用户痛点，为后续改版提供依据  \n",
    "\n",
    "---\n",
    "\n",
    "**簇 1 留存洞察：**\n",
    "\n",
    "- **留存表现 / Retention Behavior**  \n",
    "  - 期数 0 留存 100%，之后基本无复购，与簇 0 保持一致  \n",
    "  - 不过首购距今仅 ~177 天，品牌记忆尚未完全消退  \n",
    "\n",
    "- **画像回顾 / Profile Recap**  \n",
    "  - R ≈ 177 天：最近一次购买约半年之前  \n",
    "  - F = 1 笔：仅有单次消费  \n",
    "  - M ≈ 135 元：低客单价  \n",
    "\n",
    "- **运营建议 / Actionable Recommendations**  \n",
    "  1. **“半年关怀”专属触达**：在第 180 天节点推送“周年回馈”优惠券（如 15% OFF／满减券），抓住品牌还留存印象的窗口\n",
    "  2. **个性化召回**：根据用户首次购买品类，推荐相似或补充产品，利用 短信/推送通知 精准触达  \n",
    "  3. **免邮/小额优惠**：赠送一次“免邮券”或“小额返现券”（如满 100 元返 10 元），降低用户回购门槛\n",
    "\n",
    "---\n",
    "\n",
    "**簇 2 留存洞察：**\n",
    "\n",
    "- **留存表现**  \n",
    "  - 第 1 期（月）平均留存 ~20.2%：约五分之一的用户在首月复购  \n",
    "  - 第 2 期（月）~9.5%，第 3 期 ~7.2%，第 4 期 ~6.8%：复购率迅速回落至中下水平  \n",
    "  - 第 5–6 期维持在 ~5–6%：形成稳定的中期复购 “中坚” 群体  \n",
    "  - 第 7–12 期长尾留存 ~4–5%：少量高粘性用户持续贡献价值  \n",
    "\n",
    "- **画像回顾**  \n",
    "  - R ≈ 268 天：用户距离最后一次购买较长  \n",
    "  - F ≈ 2.1 笔：平均发生 2 次以上交易  \n",
    "  - M ≈ 290 元：中等消费水平  \n",
    "\n",
    "- **运营建议**  \n",
    "  1. **首月深度运营**：第 1 期后留存率最大（20%），可在 0–30 天内推「组合优惠包」或「满减券」，提升第 2 次交易意愿  \n",
    "  2. **中期粘性培养**：第 2–4 期（6–12 周）进行积分激励或小额专属折扣，稳固中期活跃用户  \n",
    "  3. **长尾价值挖掘**：对第 5 期后依旧活跃的 ~5% 用户，重点推荐新品或 VIP 专属权益，激励其成为品牌忠实用户  \n",
    "  4. **差异化测试**：基于地域或品类偏好，A/B 测试不同优惠形式（满减 vs. 捆绑）和渠道（App 推送 vs. EDM），找到对复购影响最大的方案  \n",
    "\n",
    "---\n",
    "\n",
    "**簇 3 留存洞察：**\n",
    "\n",
    "- **留存表现 / Retention Behavior**  \n",
    "  - 第 1 期（月）留存 ~1.34%、第 2 期 ~1.01%、第 3 期 ~0.51%：首月之后几乎不复购  \n",
    "  - 第 5–6 期小幅回升至 ~1.35–1.41%：少数用户在中长期出现二次高额购买  \n",
    "\n",
    "- **画像回顾 / Profile Recap**  \n",
    "  - R ≈ 288 天：较久未复购，但并非完全流失  \n",
    "  - F = 1 笔：以单次高额消费为主  \n",
    "  - M ≈ 1,196 元：一次性大额客单  \n",
    "\n",
    "- **运营建议 / Actionable Recommendations**  \n",
    "  1. **高端定制关怀**：在第 4–5 月节点推送专属 VIP 权益（如新品预览、免费配送），利用二次购高价值用户的平均复购时机  \n",
    "  2. **个性化推荐**：基于首单品类和所处地区，精准推送相似或升级款产品  \n",
    "  3. **会员/礼遇升级**：对 Cluster 3 用户开放限时邀请制会员或积分加速通道，强化品牌黏性  \n",
    "  4. **高触达渠道**：电话/微信专属客服或一对一邮件，提高高净值用户的专属感与二次购买意愿  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb850ac",
   "metadata": {
    "papermill": {
     "duration": 0.014373,
     "end_time": "2025-07-17T23:51:36.251773",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.237400",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. 简版 LTV 预测 / Simplified LTV Prediction  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab8544d",
   "metadata": {
    "papermill": {
     "duration": 0.013555,
     "end_time": "2025-07-17T23:51:36.279734",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.266179",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.1 划分时间窗口 & 基于 cutoff_date 计算特征 / Split by cutoff_date & compute features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "385ca4f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:36.309221Z",
     "iopub.status.busy": "2025-07-17T23:51:36.308112Z",
     "iopub.status.idle": "2025-07-17T23:51:54.282046Z",
     "shell.execute_reply": "2025-07-17T23:51:54.281007Z"
    },
    "papermill": {
     "duration": 17.990326,
     "end_time": "2025-07-17T23:51:54.283647",
     "exception": false,
     "start_time": "2025-07-17T23:51:36.293321",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 1) 定义时间窗口 / Define time window\n",
    "max_date = orders_payments['order_purchase_timestamp'].max()\n",
    "cutoff_date = max_date - DateOffset(months=6)\n",
    "label_end = cutoff_date + DateOffset(months=6)\n",
    "\n",
    "# 2) 划分数据集 / Split dataset\n",
    "train_orders  = orders_payments[orders_payments['order_purchase_timestamp'] <= cutoff_date]\n",
    "future_orders = orders_payments[\n",
    "    (orders_payments['order_purchase_timestamp'] > cutoff_date) &\n",
    "    (orders_payments['order_purchase_timestamp'] <= label_end)\n",
    "]\n",
    "\n",
    "# 3) 基于 train_orders 计算 RFM / Compute RFM features on train_orders\n",
    "reference_date = cutoff_date\n",
    "rfm_train = (\n",
    "    train_orders\n",
    "    .groupby('customer_unique_id')\n",
    "    .agg(\n",
    "        R = ('order_purchase_timestamp', lambda x: (reference_date - x.max()).days),\n",
    "        F = ('order_id', 'nunique'),\n",
    "        M = ('payment_value', 'sum')\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "# 4) 标准化 & 聚类标签 / Scale & cluster label\n",
    "rfm_train[['R_z', 'F_z', 'M_z']] = scaler.transform(rfm_train[['R','F','M']])\n",
    "rfm_train['cluster'] = kmeans_model.predict(rfm_train[['R_z','F_z','M_z']])\n",
    "\n",
    "# 5) 基于 train_orders 计算其他特征 / Compute other features on train_orders\n",
    "# 计算品类多样度 / Compute category diversity per user\n",
    "order_items_train = order_items_products.merge(\n",
    "    train_orders[['order_id', 'customer_unique_id']],\n",
    "    on='order_id',\n",
    "    how='inner'\n",
    ")\n",
    "category_count = (\n",
    "    order_items_train\n",
    "    .groupby('customer_unique_id')['product_category_name']\n",
    "    .nunique()\n",
    "    .reset_index(name='category_count')\n",
    ")\n",
    "\n",
    "# 最常用支付方式 / Most frequent payment type\n",
    "payment_mode = (\n",
    "    train_orders\n",
    "    .groupby('customer_unique_id')['payment_type']\n",
    "    .agg(lambda x: x.mode().iloc[0])\n",
    "    .reset_index(name='payment_type')\n",
    ")\n",
    "\n",
    "# 所在省份 / Most frequent state\n",
    "state_mode = (\n",
    "    train_orders\n",
    "    .groupby('customer_unique_id')['customer_state']\n",
    "    .agg(lambda x: x.mode().iloc[0])\n",
    "    .reset_index(name='customer_state')\n",
    ")\n",
    "\n",
    "# 6) 构建特征表 / Merge all features\n",
    "features_train = (\n",
    "    rfm_train[['customer_unique_id','R_z','F_z','M_z','cluster']]\n",
    "    .merge(category_count, on='customer_unique_id', how='left')\n",
    "    .merge(payment_mode, on='customer_unique_id', how='left')\n",
    "    .merge(state_mode, on='customer_unique_id', how='left')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ae74ef1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.314324Z",
     "iopub.status.busy": "2025-07-17T23:51:54.314052Z",
     "iopub.status.idle": "2025-07-17T23:51:54.335645Z",
     "shell.execute_reply": "2025-07-17T23:51:54.334675Z"
    },
    "papermill": {
     "duration": 0.039204,
     "end_time": "2025-07-17T23:51:54.337342",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.298138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_unique_id      0\n",
       "R_z                     0\n",
       "F_z                     0\n",
       "M_z                     0\n",
       "cluster                 0\n",
       "category_count        594\n",
       "payment_type            0\n",
       "customer_state          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查 merge 后可能出现的缺失值 / Check possible missing values after merge\n",
    "features_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70cfd2fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.367271Z",
     "iopub.status.busy": "2025-07-17T23:51:54.366987Z",
     "iopub.status.idle": "2025-07-17T23:51:54.409280Z",
     "shell.execute_reply": "2025-07-17T23:51:54.408378Z"
    },
    "papermill": {
     "duration": 0.058574,
     "end_time": "2025-07-17T23:51:54.410866",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.352292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 customer_unique_id       R_z       F_z       M_z  cluster  \\\n",
       "0  0000f46a3911fa3c0805444483337064  0.744811 -0.162368 -0.347291        0   \n",
       "1  0000f6ccb0745a6a4b88665a16c9f078 -0.663145 -0.162368 -0.531366        1   \n",
       "2  0004aac84e0df4da2b147fca70cf8255 -0.878250 -0.162368  0.130916        1   \n",
       "3  0004bd2a26a76fe21f786e4fbd80607f -1.803850 -0.162368  0.001674        1   \n",
       "4  00053a61a98854899e70ed204dd4bafe -1.562673 -0.162368  1.091434        1   \n",
       "\n",
       "   category_count  payment_type_credit_card  payment_type_debit_card  \\\n",
       "0             1.0                      True                    False   \n",
       "1             1.0                      True                    False   \n",
       "2             1.0                      True                    False   \n",
       "3             1.0                      True                    False   \n",
       "4             1.0                      True                    False   \n",
       "\n",
       "   payment_type_unknown  payment_type_voucher  ...  customer_state_PR  \\\n",
       "0                 False                 False  ...              False   \n",
       "1                 False                 False  ...              False   \n",
       "2                 False                 False  ...              False   \n",
       "3                 False                 False  ...              False   \n",
       "4                 False                 False  ...               True   \n",
       "\n",
       "   customer_state_RJ  customer_state_RN  customer_state_RO  customer_state_RR  \\\n",
       "0              False              False              False              False   \n",
       "1              False              False              False              False   \n",
       "2              False              False              False              False   \n",
       "3              False              False              False              False   \n",
       "4              False              False              False              False   \n",
       "\n",
       "   customer_state_RS  customer_state_SC  customer_state_SE  customer_state_SP  \\\n",
       "0              False               True              False              False   \n",
       "1              False              False              False              False   \n",
       "2              False              False              False               True   \n",
       "3              False              False              False               True   \n",
       "4              False              False              False              False   \n",
       "\n",
       "   customer_state_TO  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  \n",
       "3              False  \n",
       "4              False  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7) 处理缺失 & One-hot 编码 / Fill NAs & one-hot encode\n",
    "features_train['category_count'] = features_train['category_count'].fillna(0)\n",
    "\n",
    "features_train = pd.get_dummies(\n",
    "    features_train,\n",
    "    columns=['payment_type','customer_state'],\n",
    "    drop_first=True\n",
    ")\n",
    "\n",
    "features_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d616edd8",
   "metadata": {
    "papermill": {
     "duration": 0.014858,
     "end_time": "2025-07-17T23:51:54.440184",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.425326",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.2 准备模型输入 X, y / Prepare model input X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e6f27513",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.471701Z",
     "iopub.status.busy": "2025-07-17T23:51:54.471087Z",
     "iopub.status.idle": "2025-07-17T23:51:54.535998Z",
     "shell.execute_reply": "2025-07-17T23:51:54.535082Z"
    },
    "papermill": {
     "duration": 0.082707,
     "end_time": "2025-07-17T23:51:54.537646",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.454939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 生成标签 y：未来 6 个月 GMV / Generate label y: 6‑month future GMV\n",
    "y = (\n",
    "    future_orders\n",
    "    .groupby('customer_unique_id')['payment_value']\n",
    "    .sum()\n",
    "    # 重新索引，使 y 的顺序与 features_train 中的 customer_unique_id 一致；\n",
    "    # 对于未来没有订单的客户填充 0，表示其未来消费为 0\n",
    "    # Reindex to match the order of customer IDs in features_train;\n",
    "    # fill missing customers (i.e., no future orders) with 0.\n",
    "    .reindex(features_train['customer_unique_id'], fill_value=0)\n",
    "    .reset_index(name='y')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0a68431",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.567109Z",
     "iopub.status.busy": "2025-07-17T23:51:54.566770Z",
     "iopub.status.idle": "2025-07-17T23:51:54.605019Z",
     "shell.execute_reply": "2025-07-17T23:51:54.603839Z"
    },
    "papermill": {
     "duration": 0.054767,
     "end_time": "2025-07-17T23:51:54.606449",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.551682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features shape: (68169, 35), Label shape: (68169,)\n"
     ]
    }
   ],
   "source": [
    "# 合并特征与 y（如果尚未合并）/ Merge features and target\n",
    "model_data = features_train.merge(y, on='customer_unique_id', how='left')\n",
    "\n",
    "# 准备特征矩阵 X 与目标 y / Prepare X and y\n",
    "X = model_data.drop(columns=['customer_unique_id', 'y'])\n",
    "y = model_data['y']\n",
    "\n",
    "print(f\"Features shape: {X.shape}, Label shape: {y.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "54fb9c27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.637800Z",
     "iopub.status.busy": "2025-07-17T23:51:54.637482Z",
     "iopub.status.idle": "2025-07-17T23:51:54.652059Z",
     "shell.execute_reply": "2025-07-17T23:51:54.651090Z"
    },
    "papermill": {
     "duration": 0.033113,
     "end_time": "2025-07-17T23:51:54.653781",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.620668",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 划分训练集与测试集 / Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9126ca0",
   "metadata": {
    "papermill": {
     "duration": 0.01535,
     "end_time": "2025-07-17T23:51:54.684452",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.669102",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.3 构建回归模型并预测未来 6 个月 GMV / Build regression model & predict 6‑month GMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "05d853a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:54.715861Z",
     "iopub.status.busy": "2025-07-17T23:51:54.715522Z",
     "iopub.status.idle": "2025-07-17T23:51:55.091914Z",
     "shell.execute_reply": "2025-07-17T23:51:55.090962Z"
    },
    "papermill": {
     "duration": 0.393717,
     "end_time": "2025-07-17T23:51:55.093478",
     "exception": false,
     "start_time": "2025-07-17T23:51:54.699761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005751 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 585\n",
      "[LightGBM] [Info] Number of data points in the train set: 54535, number of used features: 34\n",
      "[LightGBM] [Info] Start training from score 1.293845\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_true</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "       y_true    y_pred\n",
       "24788     0.0  1.261308\n",
       "32388     0.0  0.867113\n",
       "60853     0.0  0.468414\n",
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      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化并训练 LightGBM 回归模型 / Initialize & train LGBMRegressor\n",
    "lgbm = LGBMRegressor(random_state=42)\n",
    "lgbm.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上预测 / Predict on test set\n",
    "y_pred = lgbm.predict(X_test)\n",
    "\n",
    "# 显示部分预测值对比 / Display sample of predictions\n",
    "pd.DataFrame({'y_true': y_test, 'y_pred': y_pred}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c71c559",
   "metadata": {
    "papermill": {
     "duration": 0.013959,
     "end_time": "2025-07-17T23:51:55.121796",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.107837",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.4 模型评估：MAE / R² / Evaluate model (MAE, R²)\n",
    "\n",
    "**选择 MAE 的原因**\n",
    "\n",
    "- 业务可解释性强：MAE 的单位与 GMV 一致（元），让运营能直观理解“平均误差$\\pm$X元”的含义。  \n",
    "\n",
    "- 对极端值不敏感：MAE 不会因为少数大额长尾用户的极端误差而被过度放大，能更真实地反映整体预测表现。  \n",
    "\n",
    "- 优化目标明确：MAE 平等对待每个样本的误差，更契合“追求整体稳定性”的建模需求。  \n",
    "\n",
    "--- \n",
    "\n",
    "**Why MAE**\n",
    "\n",
    "- **Strong business interpretability**: MAE is in the same unit as GMV (currency), making it easy for stakeholders to grasp what an average error of ±X means.\n",
    "\n",
    "- **Robust to outliers**: MAE does not overly weight large errors from a few high-value users, providing a more realistic measure of overall predictive accuracy.\n",
    "\n",
    "- **Clear optimization objective**: MAE treats all errors equally, aligning with the goal of achieving stable, consistent predictions across the user base.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1a9a182",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:55.151170Z",
     "iopub.status.busy": "2025-07-17T23:51:55.150907Z",
     "iopub.status.idle": "2025-07-17T23:51:55.160550Z",
     "shell.execute_reply": "2025-07-17T23:51:55.159737Z"
    },
    "papermill": {
     "duration": 0.026026,
     "end_time": "2025-07-17T23:51:55.161930",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.135904",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.95\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f\"MAE: {mae:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0557f99",
   "metadata": {
    "papermill": {
     "duration": 0.014354,
     "end_time": "2025-07-17T23:51:55.190614",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.176260",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**解读 / Interpreting**\n",
    "\n",
    "- **绝对意义**：平均预测误差约为 2.95，表示模型对每个用户未来 6 个月 GMV 的预测，平均偏差不到 3 元。  \n",
    "- **零值主导**：由于大多数用户未来半年的 GMV 为 0（不再购买），模型只要预测接近 0 就能拿到很低的 MAE。  \n",
    "- **对关键用户的意义**：对实际 y > 0 的活跃用户，平均 2.95 元的误差可能是“可忽略”级别，也可能不够精准，需单独评估。  \n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "**Interpreting**\n",
    "\n",
    "- **Absolute sense**: The average prediction error is about 2.95 (BRL), meaning on average our model’s forecast for a user’s 6‑month GMV is within ±3 reais of the true value.\n",
    "- **Zero‑dominance**: Because most users do not purchase again (true GMV = 0), predicting near zero yields a very low MAE overall.\n",
    "- **For active customers**: For users with true y > 0, an average 2.95 error may be trivial or still too large — this subgroup deserves a separate MAE calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "98816ba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-17T23:51:55.220909Z",
     "iopub.status.busy": "2025-07-17T23:51:55.220538Z",
     "iopub.status.idle": "2025-07-17T23:51:55.227580Z",
     "shell.execute_reply": "2025-07-17T23:51:55.226790Z"
    },
    "papermill": {
     "duration": 0.024316,
     "end_time": "2025-07-17T23:51:55.229379",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.205063",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active users MAE: 149.20\n"
     ]
    }
   ],
   "source": [
    "mask = y_test > 0\n",
    "mae_active = mean_absolute_error(y_test[mask], y_pred[mask])\n",
    "print(f\"Active users MAE: {mae_active:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49ed1cf",
   "metadata": {
    "papermill": {
     "duration": 0.014974,
     "end_time": "2025-07-17T23:51:55.259389",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.244415",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Active Users MAE 解读**：\n",
    "\n",
    "- **误差规模**：活跃用户（未来 6 个月内有消费）的平均预测误差约为 149.20 元，远高于整体 MAE 2.95 元，表明模型对高价值复购用户的预测能力仍显不足。  \n",
    "- **偏向零值**：模型在多数用户（GMV=0）上表现优异，但这种偏向零的“好成绩”掩盖了对小部分高价值用户的大误差。  \n",
    "\n",
    "---\n",
    "\n",
    "**Interpreting Active Users MAE**:\n",
    "\n",
    "- **Error magnitude**: The average error for active users is ~149.20 BRL—much larger than the overall MAE of 2.95 BRL—indicating poor predictive performance on users who actually repurchase.\n",
    "- **Zero‑bias**: The model’s strong performance on zero‑spending users masks its large errors on the small subset of high‑value customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842f590d",
   "metadata": {
    "papermill": {
     "duration": 0.014377,
     "end_time": "2025-07-17T23:51:55.288498",
     "exception": false,
     "start_time": "2025-07-17T23:51:55.274121",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.5 下一步优化建议 / Next Steps for Improvement\n",
    "\n",
    "1. **目标转换（Log1p）**  \n",
    "   - 对 y 进行 `log1p(y)` 转换后建模，再 `expm1(pred)` 还原，能减轻长尾对模型的影响。  \n",
    "\n",
    "2. **分层建模（Two‑stage）**  \n",
    "   - **第一步**：二分类模型预测用户是否会复购（y>0）。  \n",
    "   - **第二步**：仅对预测为正的样本用回归模型预测具体 GMV，可专门优化高价值用户。  \n",
    "\n",
    "3. **丰富特征**  \n",
    "   - 加入更多行为信号：如用户评论得分、发货/配送时长、历史购买间隔分布等。  \n",
    "\n",
    "4. **调整评估指标**  \n",
    "   - 增加 **RMSE** 或 **RMSLE**，观察对大误差或对数误差的敏感度；并用 **分层 MAE**（按 GMV 档位）衡量不同价值段表现。  \n",
    "\n",
    "5. **交叉验证与调参**  \n",
    "   - 使用 K‑Fold CV 确保模型稳定性；在 LightGBM 上尝试优化学习率、树深、叶子节点数等超参。  \n",
    "\n",
    "通过上述措施，可显著提升模型对高价值复购用户的预测精度，同时保持对零值用户的良好表现。  \n",
    "\n",
    "---\n",
    "\n",
    "1. Transform target with `log1p` to reduce heavy‑tail impact, then inverse with `expm1`.\n",
    "\n",
    "2. Use a two‑stage approach: first classify purchase/no‑purchase, then regress only on positive samples.\n",
    "\n",
    "3. Add features such as review score, delivery time, purchase interval stats.\n",
    "\n",
    "4. Include RMSE/RMSLE and segmented MAE by spending tiers.\n",
    "\n",
    "5. Perform K‑Fold CV and hyperparameter tuning on learning rate, max_depth, num_leaves, etc.\n",
    "\n",
    "Implementing these steps should improve predictive accuracy for high‑value customers while retaining performance on zero‑spending users."
   ]
  }
 ],
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